Ownership

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Ownership: Nike is mainly owned by institutional investors, who hold around 68% of all shares. The 10 largest shareholders of Nike in early 2024 were:

[["Nike,_Inc.", "has_shares_held_by", "Institutional_investor"], ["Nike,_Inc.", "shares_held_by", "68.0"], ["Nike,_Inc.", "largest_shareholders", "\"The 10 largest shareholders of Nike in early 2024 were:\""]]


Controversies

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Controversies: Nike has contracted with more than 700 shops around the world and has offices located in 45 countries outside the United States.Most of the factories are located in Asia, including Indonesia, China, Taiwan, India,Thailand, Vietnam, Pakistan, Philippines, and Malaysia.Nike is hesitant to disclose information about the contract companies it works with. However, due to harsh criticism from some organizations like CorpWatch, Nike has disclosed information about its contract factories in its Corporate Governance Report.

[["Nike,_Inc", "Company"], ["700", "number"], ["Asia", "Place"], ["Nike,_Inc", "Product"], ["Corporate_Governance_Report", "Report"], ["United_States", "Place"], ["45", "number"], ["Nike,_Inc", "Controversy"], ["CorpWatch", "Organisation"]]


Controversies

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Controversies: Nike has contracted with more than 700 shops around the world and has offices located in 45 countries outside the United States.Most of the factories are located in Asia, including Indonesia, China, Taiwan, India,Thailand, Vietnam, Pakistan, Philippines, and Malaysia.Nike is hesitant to disclose information about the contract companies it works with. However, due to harsh criticism from some organizations like CorpWatch, Nike has disclosed information about its contract factories in its Corporate Governance Report.

[["Nike,_Inc", "has_contracted_with", "700+"], ["Nike,_Inc", "has_offices_in", "45 countries outside the United States"], ["Nike,_Inc", "has_factories_in", "Asia"], ["Nike,_Inc", "has_factories_in", "Indonesia"], ["Nike,_Inc", "has_factories_in", "Vietnam"], ["Nike,_Inc", "has_factories_in", "Philippines"], ["Nike,_Inc", "has_factories_in", "Malaysia"], ["Nike,_Inc", "has_factories_in", "Pakistan"], ["Nike,_Inc", "has_factories_in", "Taiwan"], ["Nike,_Inc", "has_factories_in", "China"], ["Nike,_Inc", "has_factories_in", "India"], ["Nike,_Inc", "has_factories_in", "Thailand"], ["Nike,_Inc", "has_factories_in", "Corporation_Watch"], ["Nike,_Inc", "has_factories_in", "Corporate_Governance_Report"]]


Sweatshops

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sweatshops: In the 1990s, Nike received criticism for its use ofsweatshops.Beginning in 1990, many protests occurred in big cities such as Los Angeles,Washington, DC and Boston in order to show public outcry for Nike's use of child labor and sweatshops. Nike has been criticized for contracting with factories (known asNike sweatshops) in countries such as China, Vietnam, Indonesia and Mexico. Vietnam Labor Watch, an activist group, has documented that factories contracted by Nike haveviolatedminimum wageand overtimelaws in Vietnamas late as 1996, although Nike claims that this practice has been stopped. As of July 2011, Nike stated that two-thirds of its factories producing Converse products still do not meet the company's standards for worker treatment. A July 2011Associated Pressarticle stated that employees at the company's plants in Indonesia reported constant abuse from supervisors.

[["Nike,_Inc.", "Company"], ["Sweatshops", "Issue"], ["1990", "Year"], ["Vietnam_Labor_Watch", "Company"], ["Vietnam", "Place"], ["\"Two-thirds of the factories producing Converse products still do not meet the company's standards for worker treatment\"", "string"], ["Associated_Press", "Company"], ["2011-07-01", "Date"], ["Indonesia", "Place"]]


Sweatshops

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sweatshops: In the 1990s, Nike received criticism for its use ofsweatshops.Beginning in 1990, many protests occurred in big cities such as Los Angeles,Washington, DC and Boston in order to show public outcry for Nike's use of child labor and sweatshops. Nike has been criticized for contracting with factories (known asNike sweatshops) in countries such as China, Vietnam, Indonesia and Mexico. Vietnam Labor Watch, an activist group, has documented that factories contracted by Nike haveviolatedminimum wageand overtimelaws in Vietnamas late as 1996, although Nike claims that this practice has been stopped. As of July 2011, Nike stated that two-thirds of its factories producing Converse products still do not meet the company's standards for worker treatment. A July 2011Associated Pressarticle stated that employees at the company's plants in Indonesia reported constant abuse from supervisors.

[["Nike,_Inc.", "criticism", "Sweatshops"], ["Nike,_Inc.", "has_product_line", "Converse"], ["Nike,_Inc.", "occurred_in_year", "1990"], ["Nike,_Inc.", "occurred_in_year", "1996"], ["Nike,_Inc.", "has_product_line", "Nike"], ["Nike,_Inc.", "criticism", "\"Employees at the company's plants in Indonesia reported constant abuse from supervisors.\""], ["Nike,_Inc.", "occurred_in_year", "2011-07-01"], ["Nike,_Inc.", "criticism", "\"Two-thirds of its factories producing Converse products still do not meet the company's standards for worker treatment.\""], ["Nike,_Inc.", "criticism", "\"Vietnam Labor Watch, an activist group, has documented that factories contracted by Nike have violated minimum wage and overtime laws in Vietnam as late as 1996.\""], ["Nike,_Inc.", "criticism", "\"Beginning in 1990, many protests occurred in big cities such as Los Angeles, Washington, DC and Boston in order to show public outcry for Nike's use of child labor and sweatshops.\""]]


Child labor

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Child labor: During the 1990s, Nike faced criticism for the use ofchild labor in CambodiaandPakistanin factories it contracted to manufacture soccer balls. Although Nike took action to curb or at least reduce the practice, they continue to contract their production to companies that operate in areas where inadequate regulation and monitoring make it hard to ensure that child labor is not being used. In 2001, a BBC documentary uncovered occurrences of child labor and poor working conditions in a Cambodian factory used by Nike.The documentary focused on six girls, who all worked seven days a week, often 16 hours a day.

[["Nike,_Inc", "Company"], ["Cambodia", "Place"], ["BBC", "Company"], ["Cambodia", "Company"], ["2001", "Date"], ["Pakistan", "Place"], ["Soccer_ball", "Product"], ["Six_Girls", "Film"], ["Cambodia", "Company"], ["Child_labor", "Issue"], ["Six_Girls", "Company"], ["Cambodia", "Place"], ["Soccer_ball", "Product"], ["1990s", "Time"], ["BBC", "Company"], ["Cambodia", "Place"], ["Six_Girls", "Film"], ["Nike,_Inc", "Company"], ["Cambodia", "Company"], ["Soccer_ball", "Product"], ["Six_Girls", "Company"], ["Cambodia", "Place"], ["Child_labor", "Issue"], ["BBC", "Company"], ["2001", "Date"], ["Cambodia", "Company"], ["Soccer_ball", "Product"], ["Six_Girls", "Film"], ["Cambodia", "Place"], ["Child_labor", "Issue"], ["Cambodia", "Company"], ["Soccer_ball", "Product"], ["Six_Girls", "Company"], ["Cambodia", "Place"], ["Child_labor", "Issue"], ["BBC", "Company"], ["2001", "Date"]]


Child labor

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Child labor: During the 1990s, Nike faced criticism for the use ofchild labor in CambodiaandPakistanin factories it contracted to manufacture soccer balls. Although Nike took action to curb or at least reduce the practice, they continue to contract their production to companies that operate in areas where inadequate regulation and monitoring make it hard to ensure that child labor is not being used. In 2001, a BBC documentary uncovered occurrences of child labor and poor working conditions in a Cambodian factory used by Nike.The documentary focused on six girls, who all worked seven days a week, often 16 hours a day.

[["Nike,_Inc", "has_product_line", "Soccer_balls"], ["Nike,_Inc", "child_labor", "\"Cambodia and Pakistan\""], ["Nike,_Inc", "child_labor", "\"During the 1990s, Nike faced criticism for the use of child labor in factories it contracted to manufacture soccer balls. Although Nike took action to curb or at least reduce the practice, they continue to contract their production to companies that operate in areas where inadequate regulation and monitoring make it hard to ensure that child labor is not being used.\""], ["Nike,_Inc", "child_labor", "\"BBC broadcast a documentary in 2001 which uncovered occurrences of child labour and poor working conditions in a Cambodian factory used by Nike. The documentary focused on six girls, who all worked seven days a week, often 16 hours a day.\""]]


Strike in China factory

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Strike in China factory: In April 2014, one of the biggest strikes in mainland China took place at theYue Yuen Industrial HoldingsDongguanshoe factory, producing amongst others for Nike. Yue Yuen did underpay an employee by 250 yuan (40.82 US Dollars) per month. The average salary at Yue Yuen is 3000 yuan per month. The factory employs 70,000 people. This practice was in place for nearly 20 years.


Strike in China factory

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Strike in China factory: In April 2014, one of the biggest strikes in mainland China took place at theYue Yuen Industrial HoldingsDongguanshoe factory, producing amongst others for Nike. Yue Yuen did underpay an employee by 250 yuan (40.82 US Dollars) per month. The average salary at Yue Yuen is 3000 yuan per month. The factory employs 70,000 people. This practice was in place for nearly 20 years.

[["Nike,_Inc", "produces", "Yue_Yuen_Industrial_Holdings"], ["Yue_Yuen_Industrial_Holdings", "has_product_line", "Shoe"], ["Yue_Yuen_Industrial_Holdings", "had_revenue_of", "3000.0 (yuan)"], ["Yue_Yuen_Industrial_Holdings", "produces", "Shoe"], ["Yue_Yuen_Industrial_Holdings", "had_revenue_of", "250.0 (yuan)"], ["Yue_Yuen_Industrial_Holdings", "number_of_employees", "70000"], ["Yue_Yuen_Industrial_Holdings", "strike", "\"April 2014, one of the biggest strikes in mainland China\""], ["Yue_Yuen_Industrial_Holdings", "strike", "\"underpaying an employee by 250 yuan (40.82 US Dollars) per month\""], ["Yue_Yuen_Industrial_Holdings", "strike", "\"practice in place for nearly 20 years\""]]


Paradise Papers

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Paradise Papers: On November 5, 2017, theParadise Papers, a set of confidentialelectronic documentsrelating tooffshore investment, revealed that Nike is among the corporations that usedoffshore companiesto avoid taxes. Applebydocuments detail how Nike boosted its after-tax profits by, among other maneuvers, transferring ownership of its Swoosh trademark to a Bermudan subsidiary, Nike International Ltd. This transfer allowed the subsidiary to charge royalties to its European headquarters inHilversum,Netherlands, effectively converting taxable companyprofitsto an account payable intax-freeBermuda.Although the subsidiary was effectively run by executives at Nike's main offices in Beaverton, Oregon—to the point where a duplicate of the Bermudan company's seal was needed—for tax purposes the subsidiary was treated as Bermuda. Its profits were not declared in Europe and came to light only because of a mostly unrelated case in US Tax Court, where papers filed by Nike briefly mention royalties in 2010, 2011 and 2012 totaling $3.86 billion.Under an arrangement with Dutch authorities, the tax break was to expire in 2014, so another reorganization transferred the intellectual property from the Bermudan company to a Dutchcommanditaire vennootschapor limited partnership, Nike Innovate CV. Dutch law treats income earned by a CV as if it had been earned by the principals, who owe no tax in the Netherlands if they do not reside there.

[["Nike,_Inc.", "Company"], ["Paradise_Papers", "Company"], ["2017-11-05", "Date"], ["Nike_Innovate_C.V.", "Company"], ["Bermuda", "Place"], ["Nike,_Inc.", "Person"], ["Hilversum", "City"], ["United_States", "Country"], ["Nike,_Inc.", "Product"], ["Swoosh", "Product"], ["Taxation", "Issue"], ["Nike,_Inc.", "Company"], ["United_States", "Country"], ["Nike,_Inc.", "Person"], ["Bermuda", "Place"], ["Nike,_Inc.", "Company"], ["3.86 (billion dollars)", "number"], ["Nike,_Inc.", "Company"], ["United_States", "Country"], ["Nike,_Inc.", "Person"], ["Bermuda", "Place"], ["Nike,_Inc.", "Company"], ["Swoosh", "Product"], ["Nike,_Inc.", "Company"], ["Nike_Innovate_C.V.", "Company"], ["Bermuda", "Place"], ["Nike,_Inc.", "Company"], ["Hilversum", "City"], ["United_States", "Country"], ["Nike,_Inc.", "Person"], ["Bermuda", "Place"], ["Nike,_Inc.", "Company"], ["Swoosh", "Product"], ["Nike,_Inc.", "Company"], ["Nike_Innovate_C.V.", "Company"], ["Bermuda", "Place"], ["Nike,_Inc.", "Person"], ["Hilversum", "City"], ["United_States", "Country"], ["Nike,_Inc.", "Company"], ["Swoosh", "Product"], ["Nike,_Inc.", "Company"], ["Nike_Innovate_C.V.", "Company"], ["Bermuda", "Place"], ["Nike,_Inc.", "Person"], ["Hilversum", "City"], ["United_States", "Country"], ["Nike,_Inc.", "Company"], ["Swoosh", "Product"], ["Nike,_Inc.", "Company"], ["Nike_Innovate_C.V.", "Company"], ["Bermuda", "Place"], ["Nike,_Inc.", "Person"], ["Hilversum", "City"], ["United_States", "Country"]]


Paradise Papers

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Paradise Papers: On November 5, 2017, theParadise Papers, a set of confidentialelectronic documentsrelating tooffshore investment, revealed that Nike is among the corporations that usedoffshore companiesto avoid taxes. Applebydocuments detail how Nike boosted its after-tax profits by, among other maneuvers, transferring ownership of its Swoosh trademark to a Bermudan subsidiary, Nike International Ltd. This transfer allowed the subsidiary to charge royalties to its European headquarters inHilversum,Netherlands, effectively converting taxable companyprofitsto an account payable intax-freeBermuda.Although the subsidiary was effectively run by executives at Nike's main offices in Beaverton, Oregon—to the point where a duplicate of the Bermudan company's seal was needed—for tax purposes the subsidiary was treated as Bermuda. Its profits were not declared in Europe and came to light only because of a mostly unrelated case in US Tax Court, where papers filed by Nike briefly mention royalties in 2010, 2011 and 2012 totaling $3.86 billion.Under an arrangement with Dutch authorities, the tax break was to expire in 2014, so another reorganization transferred the intellectual property from the Bermudan company to a Dutchcommanditaire vennootschapor limited partnership, Nike Innovate CV. Dutch law treats income earned by a CV as if it had been earned by the principals, who owe no tax in the Netherlands if they do not reside there.

[["Nike,_Inc.", "has_trademark", "Swoosh"], ["Nike,_Inc.", "revealed_by", "Paradise_Papers"], ["Nike,_Inc.", "subsidiary", "Nike_Innovate_C.V."], ["Nike,_Inc.", "subsidiary", "Nike_International"], ["Nike,_Inc.", "has_product_line", "Swoosh"], ["Nike,_Inc.", "location", "Hilversum"], ["Nike,_Inc.", "subsidiary", "Nike_International_Ltd."], ["Nike,_Inc.", "taxation", "Bermuda"], ["Nike,_Inc.", "revenue", "3846000000"], ["Nike,_Inc.", "subsidiary", "Appleby"], ["Nike,_Inc.", "location", "Beaverton,_Oregon"], ["Nike,_Inc.", "taxation", "Netherlands"], ["Nike,_Inc.", "revenue", "2010"], ["Nike,_Inc.", "revenue", "2011"], ["Nike,_Inc.", "revenue", "2012"]]


Colin Kaepernick

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Colin Kaepernick: In September 2018, Nike announced it had signed former American football quarterbackColin Kaepernick, noted for hiscontroversial decision to kneelduring the playing of the US national anthem, to a long-term advertising campaign.According to Charles Robinson ofYahoo! Sports, Kaepernick and Nike agreed to a new contract despite the fact Kaepernick has been with the company since 2011 and said that "interest from other shoe companies" played a part in the new agreement. Robinson said the contract is a "wide endorsement" where Kaepernick will have his own branded line including shoes, shirts, jerseys and more.In response, some people set fire to their own Nike-branded clothes and shoes or cut the Nike swoosh logo out of their clothes, and theFraternal Order of Policecalled the advertisement an "insult";others, such asLeBron James,Serena Williams,and theNational Black Police Association,praised Nike for its campaign. TheCollege of the Ozarksremoved Nike from all their athletic uniforms in response. During the following week, Nike's stock price fell 2.2%, even as online orders of Nike products rose 27% compared with the previous year.In the following three months, Nike reported a rise in sales. In July 2019, Nike released a shoe featuring aBetsy Ross flagcalled the Air Max 1 Quick Strike Fourth of July trainers. The trainers were designed to celebrateIndependence Day. The model was subsequently withdrawn afterColin Kaepernicktold the brand he and others found the flag offensive because of its association with slavery. Nike's decision to withdraw the product drew criticism fromArizona'sRepublican Governor,Doug Ducey, and Texas's Republican SenatorTed Cruz.Nike's decision was praised by others due to the use of the flag bywhite nationalists,but theAnti-Defamation League'sCenter on Extremism has declined to add the flag to its database of "hate symbols."

[["Nike,_Inc.", "Company"], ["Colin_Kaepernick", "Person"], ["Air_Max_1", "Product"], ["Betsy_Ross_flag", "Design"], ["2018-09-03", "Date"], ["Nike,_Inc.", "Product"], ["Air_Max_1", "Product"], ["United_States", "Country"], ["National_Black_Police_Association", "Person"], ["Fraternal_Order_of_Police", "Person"], ["LeBron_James", "Person"], ["Serena_Williams", "Person"], ["College_of_the_Ozarks", "Person"], ["United_States", "Country"], ["Anti-Defamation_League", "Organisation"], ["Air_Max_1", "Product"], ["Doug_Ducey", "Person"], ["Ted_Cruz", "Person"], ["United_States", "Country"], ["Nike,_Inc.", "Company"], ["Colin_Kaepernick", "Person"], ["2018-09-04", "Date"], ["Air_Max_1", "Product"], ["Betsy_Ross_flag", "Design"], ["United_States", "Country"], ["Nike,_Inc.", "Company"], ["LeBron_James", "Person"], ["Serena_Williams", "Person"], ["Fraternal_Order_of_Police", "Person"], ["College_of_the_Ozarks", "Person"], ["United_States", "Country"], ["Anti-Defamation_League", "Organisation"], ["Air_Max_1", "Product"], ["Doug_Ducey", "Person"], ["Ted_Cruz", "Person"], ["United_States", "Country"], ["Nike,_Inc.", "Company"], ["Colin_Kaepernick", "Person"], ["2019-07-01", "Date"]]


Colin Kaepernick

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Colin Kaepernick: In September 2018, Nike announced it had signed former American football quarterbackColin Kaepernick, noted for hiscontroversial decision to kneelduring the playing of the US national anthem, to a long-term advertising campaign.According to Charles Robinson ofYahoo! Sports, Kaepernick and Nike agreed to a new contract despite the fact Kaepernick has been with the company since 2011 and said that "interest from other shoe companies" played a part in the new agreement. Robinson said the contract is a "wide endorsement" where Kaepernick will have his own branded line including shoes, shirts, jerseys and more.In response, some people set fire to their own Nike-branded clothes and shoes or cut the Nike swoosh logo out of their clothes, and theFraternal Order of Policecalled the advertisement an "insult";others, such asLeBron James,Serena Williams,and theNational Black Police Association,praised Nike for its campaign. TheCollege of the Ozarksremoved Nike from all their athletic uniforms in response. During the following week, Nike's stock price fell 2.2%, even as online orders of Nike products rose 27% compared with the previous year.In the following three months, Nike reported a rise in sales. In July 2019, Nike released a shoe featuring aBetsy Ross flagcalled the Air Max 1 Quick Strike Fourth of July trainers. The trainers were designed to celebrateIndependence Day. The model was subsequently withdrawn afterColin Kaepernicktold the brand he and others found the flag offensive because of its association with slavery. Nike's decision to withdraw the product drew criticism fromArizona'sRepublican Governor,Doug Ducey, and Texas's Republican SenatorTed Cruz.Nike's decision was praised by others due to the use of the flag bywhite nationalists,but theAnti-Defamation League'sCenter on Extremism has declined to add the flag to its database of "hate symbols."

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Hong Kong protests

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Hong Kong protests: U.S. Vice PresidentMike Pencecriticized Nike for "siding with theChinese Communist Partyand silencing free speech". He claimed that afterHouston Rocketsgeneral managerDaryl Moreywas criticized by the Chinese government forhis tweetsupporting the2019 Hong Kong protests, Nike removed Rockets merchandise from its stores in China. On January 31, 2020, theWorld Athleticsissued new guidelines concerning shoes to be used in the upcomingTokyo 2020 Olympics.These updates came in response to criticisms concerning technology in the Nike Vaporfly running shoes, which had been submitted beginning around 2017–2018.These criticisms stated that the shoes provided athletes with an unfair advantage over their opponents and some critics considered it to be a form oftechnology doping.According to Nike funded research, the shoes can improve efficiency by up to 4.2%and runners who have tested the shoe are saying that it causes reduced soreness in the legs; sports technologist Bryce Dyer attributes this to the ZoomX and carbon fiber plate since it absorbs the energy and "spring[s] runners forward".Some athletes, scientists, and fans have compared this to the 2008LAZR swimsuit controversy. Some of the major changes in the guidelines that have come about as a result of these criticisms include that the "sole must be no thicker than 40mm" and that "the shoe must not contain more than one rigid embedded plate or blade (of any material) that runs either the full length or only part of the length of the shoe. The plate may be in more than one part but those parts must be located sequentially in one plane (not stacked or in parallel) and must not overlap". The components of the shoes are not the only thing that had major changes; starting April 30, 2020, "any shoe must have been available for purchase by any athlete on the open retail market (online or in store) for a period of four months before it can be used in competition".Prior to these new guidelines World Athletics reviewed the Vaporfly shoes and "concluded that there is independent research that indicates that the new technology incorporated in the soles of road and spiked shoes may provide a performance advantage" and that it recommends further research to "establish the true impact of [the Vaporfly] technology."

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